Cost-effective Strategies for Building Energy Efficient Mobile Applications
Bibliographic record
Abstract
Smartphone users rely on applications to perform various functionalities through their phones, but these function-alities may cause a significant drain on the device's battery. To ensure that an app does not consume unnecessary energy, app developers measure and optimize the energy consumption of their apps before releasing them to the end users. However, current optimization and measurement techniques have several limitations. The energy optimization techniques only focus on refactoring energy-greedy patterns related to system events, such as garbage collection and process switching, and on providing recommendation models for API usage. Despite the fact that the energy consumption of a single API can vary depending on its configuration, and API events account for 85% of energy con-sumption in smartphone apps, existing optimization techniques do not provide guidance on how to configure APIs for energy-efficient usage. Moreover, energy measurement techniques are cumbersome because they require developers to generate test cases and execute them on expensive, sophisticated hardware. My thesis argues that we can develop a general methodology that researchers may follow to extract energy-efficient guidelines pertaining to an API, and developers may use such guidelines to develop energy-efficient apps. Additionally, it argues that we can use static analysis to estimate an app's energy consumption. Such methodology will elevate the need for a physical smartphone and test case generation and execution. The insights and techniques that my thesis presents are particularly useful within the context of an Integrated Development Environment (IDE) or a Continu-ous Integration/Continuous Deployment (CI/CD) pipeline, where developers require results within a matter of milliseconds. Using our technique, developers would quickly receive warnings about high energy consumption caused by their code modifications, specifically those related to API usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".